WaveMix-Lite can be directly used for semantic segmentation by replacing the classifier head with deconvolution layers and a linear layer to generate the segmentation maps. On the other hand, architectural changes – such as encoder-decoder and UNet structures [1]} – are required for base CNNs and transformers, including SegFormer [2]}. The WaveMix-Lite performs on par with the other models on half-resolution Cityscapes validation set. We can see from Table REF  that WaveMix-Lite performs better than deep architectures like DeepLabV2 [3]} and SegFormer model which uses an encoder pre-trained on ImageNet-1K dataset. The low mIoU obtained by replacing the classification head of ConvMixer [4]} with segmentation head similar to WaveMix-Lite shows that token-mixing architectures which work well for classification cannot translate that performance in segmentation tasks without significant architectural modifications. This shows the versatility of our model which can provide high performance efficiently in multiple tasks. See Appendix for detailed results.
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